All flavors markdown

Pablo Mosteiro

p.mosteiro@uu.nl

Methodology & Statistics @ UU

Hanne Oberman

h.i.oberman@uu.nl

Methodology & Statistics @ UU

24 Sep 2025

Disclaimer

These materials are generated by Gerko Vink, who used to coordinate this course and holds the copyright. Gerko owes a debt of gratitude to many people as the thoughts and code in these slides are the process of years-long development cycles and discussions with his team, friends, colleagues and peers. When someone has contributed to the content of the slides, he has credited their authorship.

The intellectual property belongs to Utrecht University. Images are either directly linked, or generated with StableDiffusion or DALL-E. That said, there is no information in this presentation that exceeds legal use of copyright materials in academic settings, or that should not be part of the public domain.

Warning

You may use any and all content in this presentation - excluding names - and submit it as input to generative AI tools, with the following exception:

You must ensure that the content is not used for further training of the model

Today’s lecture

Markdown
Structure
Text
Tables & figures
Code & output
References

Take aways

Quarto markdown is tool to integrate text, figures, code and output
Quarto enables data scientists/statisticians to communicate (re)productively
There is a universe of plugins and add-ons to make your academic life easier

What is Markdown?

Markdown is a lightweight markup language for creating formatted text using plain text. It’s easy to learn and widely used in various applications.

Markdown basics

Plain text with maukup instructions (e.g. *italics*)

is converted to marked up text (e.g. italics)

Markdown flavours

GitHub-Flavored Markdown (GFM) is a variant of Markdown used on GitHub (next week), enhancing its capabilities for documentation and collaboration.
RMarkdown is an extension of Markdown that allows you to embed R code and its output directly within a document.
- Perfect for creating reproducible reports, research papers, and presentations.
Quarto is a comprehensive tool for creating reproducible and collaborative data science documents.
- Combines the power of Markdown, $\LaTeX$, and code execution to streamline document preparation.

Popular Code Editors (for `R`)

RStudio: Ideal for R programming with features tailored for data analysis and visualization.
Visual Studio Code (VS Code): A versatile code editor with a vast ecosystem of extensions for various languages.
Eclipse (with StatET): Eclipse StatET is an integrated development environment (IDE) for R, offering features for R programming and data analysis.

RStudio

`RStudio` - Integrated Development Environment (IDE) for `R`

RStudio is a powerful integrated development environment (IDE) designed specifically for the R programming language.
It offers a user-friendly interface and a suite of tools to enhance your R coding experience.

Key Features of RStudio

1. Code Editing: RStudio provides a code editor with syntax highlighting, autocompletion, and error checking, making your coding process more efficient.
2. Console: An interactive R console allows you to execute R code line by line and view results in real time.
3. Environment Pane: Keep track of your variables, data frames, and functions with the environment pane.
4. Plots and Visualizations: Create and view plots, charts, and visualizations within RStudio.
5. Integrated Help: Access R documentation, packages, and online resources directly from the IDE.
6. Version Control: Easily integrate R projects with version control systems like Git.
7. Markdown Support: RStudio seamlessly integrates with Markdown, making it an ideal choice for creating reproducible reports and documents.

It plays a crucial role in promoting reproducibility and collaboration in data science and statistical analysis.

Popular Notebook Environments

Jupyter Notebooks: Widely used interactive kernel-based computing environment for data science and machine learning, supporting multiple (i.e. almost all) programming and scripting languages.
RMarkdown: An R-based notebook environment that combines code, output, and narrative text in a single document.
Quarto: Next generation markdown for R, Python, Julia, etc.

Structure of a Quarto Document

A Quarto document is organized into blocks, which serve as the fundamental building blocks of the document’s content.
Each block can contain a combination of the following elements:
- YAML Headers: Metadata and document configuration.
- Sections and Subsections: Structuring the document into hierarchical sections for organization.
- Text: Narrative content and explanations.
- Equations: Mathematical equations and notation.
- Figures: Visualizations, charts, and images.
- Tables: Data tables for presenting results.
- Code: R code chunks for computations and data analysis.

Quarto: the YAML header

YAML (YAML Ain’t Markup Language) is a human-readable data serialization format commonly used for configuration files and metadata in various programming and markup contexts.

YAML is very simple and readable
In Quarto and many other applications, YAML is used to specify:
- Document Metadata: Information about the document itself, such as the title, author, date, and document type.
- Document Configuration: Settings related to the document’s behavior, appearance, and rendering, such as the output format (e.g., HTML, PDF), document template, and style options.
- Custom Variables: Definitions of custom variables or parameters that can be used throughout the document to control behavior or content.

Here’s an example of a simple YAML header in a Quarto document:

---
title: "A minimum example of YAML code"
---

Quarto: the YAML header

title, author, and date provide metadata about the document.
output specifies settings related to the document’s output format and theme.

The YAML header is a powerful tool for customizing and configuring Quarto documents, allowing you to control how the document is rendered and presented. It ensures that important document information and settings are stored in a human-readable and structured format at the beginning of the document.

---
title: "All flavors markdown"
author: 
  - name: Gerko Vink
    orcid: 0000-0001-9767-1924
    email: g.vink@uu.nl
    affiliations:
      - name: Methodology & Statistics @ UU University
  - name: Hanne Oberman
    orcid: 0000-0003-3276-2141
    email: h.i.oberman@uu.nl
    affiliations:
      - name: Methodology & Statistics @ UU
date: 25 Sep 2024
date-format: "D MMM YYYY"
bibliography: data/lec-2/publications.bib
execute: 
  echo: true
editor: source
format: 
  revealjs:
    embed-resources: true
    theme: [solarized, gerko.scss]
    progress: true
    multiplex: true
    transition: fade
    slide-number: true
    margin: 0.075
    logo: "images/logo.png" 
    toc: false
    toc-depth: 1
    toc-title: Outline
    scrollable: true
    reference-location: margin
    footer: Gerko Vink and Hanne Oberman - Markup Languages @ UU
---

Text in `Quarto`

Text is text. Nothing more, nothing less

Subheader

Subsubheader

# This is a heading indicating a section
## This is a heading indicating a subsection
### This is a heading indicating a subsubsection

But in the above I used

### Header
#### Subheader
##### Subsubheader

Why?

A single # denotes a section in slides

A double ## denotes a slide

It is that simple. No more framing in $\LaTeX$ or other stuff. Just use the # and ## to denote a section and a slide.

Equations

$\mu$ is used for in-line equations
$$\mu$$ is used for equations

Let’s assume that $Y$ follows a normal distribution. \[Y \sim \mathcal{N}(\mu, \sigma^2)\] Where we set in our simulations $\mu = 10$ and $\sigma^2 = 5$. We do something for every $Y_i$.

Equations

$\mu$ is used for in-line equations
$$\mu$$ is used for equations

Let’s assume that $y$ is a vector with $N$ elements such that \[y \sim \mathcal{N}(\mu, \sigma^2),\] where we set in our simulations $\mu = 10$ and $\sigma^2 = 5$. We do something for every $y_i$ with $i = 1, \dots, N$.

Figures

The above figure is left-aligned
The caption is also left-aligned

The above figure is centered
The caption is still left-aligned

The code for the figures is

![A surfing elephant](images/lec-2/elephantastic.png){fig-align="left"}
![A surfing elephant](images/lec-2/elephantastic.png){fig-align="center"}

Columns, Sizing, Equations and proper centering

A surfing $\epsilon\lambda\epsilon\phi h\alpha\nu\tau$

$e^{i\pi} + 1 = 0$

The above figure is left-aligned
The caption is also left-aligned
The equation is left-aligned too

\[e^{i\pi} + 1 = 0\]

The above figure is centered
The caption is also centered
The equation is centered

The code for the slide is

## Columns, Sizing, Equations and proper centering

::: columns
::: {.column width="50%"}
![A surfing $\epsilon\lambda\epsilon\phi h\alpha\nu\tau$](images/lec-2/elephantastic.png){fig-align="left" width="80%"}

$e^{i\pi} + 1 = 0$

The above figure is left-aligned <br>
The caption is also left-aligned <br>
The equation is left-aligned too
:::

::: {.column width="50%"}
<center>
![A surfing elephant](images/lec-2/elephantastic.png){fig-align="center" width="80%"}
</center>

$$e^{i\pi} + 1 = 0$$

The above figure is centered <br>
The caption is also centered <br>
The equation is centered
:::

:::

The code for the slide is

``` markdown
## Columns, Sizing, Equations and proper centering

::: columns
::: {.column width="50%"}
![A surfing $\epsilon\lambda\epsilon\phi h\alpha\nu\tau$](images/lec-2/elephantastic.png){fig-align="left" width="80%"}

$e^{i\pi} + 1 = 0$

The above figure is left-aligned <br>
The caption is also left-aligned <br>
The equation is left-aligned too
:::

::: {.column width="50%"}
<center>
![A surfing elephant](images/lec-2/elephantastic.png){fig-align="center" width="80%"}
</center>

$$e^{i\pi} + 1 = 0$$

The above figure is centered <br>
The caption is also centered <br>
The equation is centered
:::

:::

F-ing it all up

$e^{i\pi} + 1 = 0$

The above figure is left-aligned
The caption is also left-aligned
The equation is left-aligned too

\[e^{i\pi} + 1 = 0\]

The above figure is centered
The caption is also centered
The equation is centered

::: columns
::: {.column width="70%"}
![Rick taking a bath](images/lec-2/bathrick.png){fig-align="left" width="70%"}

$e^{i\pi} + 1 = 0$

:::

::: {.column width="30%"}
<center>
![Rick taking a bath](images/lec-2/bathrick.png){fig-align="center" width="30%"}
</center>

$$e^{i\pi} + 1 = 0$$
:::

:::

Math-ing it all up

$e^{i\pi} + 1 = 0$

\[e^{i\pi} + 1 = 0\]

## Math-ing it all up
::: columns
::: {.column width="70%"}
![Rick taking a bath](images/lec-2/goodrick.png){fig-align="left" width="30%"}

$e^{i\pi} + 1 = 0$
:::

::: {.column width="30%"}
<center>
![Rick taking a bath](images/lec-2/goodrick.png){fig-align="center" width="70%"}
</center>

$$e^{i\pi} + 1 = 0$$
:::

:::


```markdown
END OF EXAMPLE

Tables

Use https://www.tablesgenerator.com/markdown_tables if you need to make a table from scratch.

Otherwise, use tibbles:

mice::boys |> 
  tibble::as_tibble()

# A tibble: 748 × 9
     age   hgt   wgt   bmi    hc gen   phb      tv reg  
   <dbl> <dbl> <dbl> <dbl> <dbl> <ord> <ord> <int> <fct>
 1 0.035  50.1  3.65  14.5  33.7 <NA>  <NA>     NA south
 2 0.038  53.5  3.37  11.8  35   <NA>  <NA>     NA south
 3 0.057  50    3.14  12.6  35.2 <NA>  <NA>     NA south
 4 0.06   54.5  4.27  14.4  36.7 <NA>  <NA>     NA south
 5 0.062  57.5  5.03  15.2  37.3 <NA>  <NA>     NA south
 6 0.068  55.5  4.66  15.1  37   <NA>  <NA>     NA south
 7 0.068  52.5  3.81  13.8  34.9 <NA>  <NA>     NA south
 8 0.071  53    3.89  13.8  35.8 <NA>  <NA>     NA west 
 9 0.071  55.1  3.88  12.8  36.8 <NA>  <NA>     NA west 
10 0.073  54.5  4.2   14.1  38   <NA>  <NA>     NA east 
# ℹ 738 more rows

or use e.g. library(DT) for customization:

library(DT)
mtcars |> 
  datatable(options = list(pageLength = 25))

`R` figures

library(dygraphs)
dygraph(nhtemp, main = "New Haven Temperatures", ylab = "Temp (F)")

`R` figures

library(dygraphs)
dygraph(nhtemp, main = "New Haven Temperatures", ylab = "Temp (F)")

`R` figures

library(ggplot2, warn.conflicts = FALSE)
library(plotly, warn.conflicts = FALSE)
ggplot(mpg, aes(displ, hwy, colour = class)) +
  geom_point() +
  geom_smooth(se = FALSE, method = lm)

`R` figures

library(ggplot2, warn.conflicts = FALSE)
library(plotly, warn.conflicts = FALSE)
p <- ggplot(mpg, aes(displ, hwy, colour = class)) +
  geom_point() +
  geom_smooth(se = FALSE, method = lm) 
ggplotly(p)

Code chunks

We already saw some markdown code chunks. We can run inline code to calculate that 12 * 234 is 2808 by using `r ` and code chunks to evaluate larger blocks of code, such as:

Code

library(ggplot2)

ggplot(mpg, aes(x = hwy, y = cty, color = cyl)) +
  geom_point(alpha = 0.5, size = 2) +
  scale_color_viridis_c() +
  theme_minimal()

ggplot(mpg, aes(x = hwy, y = cty, color = displ)) +
  geom_point(alpha = 0.5, size = 2) +
  scale_color_viridis_c(option = "E") +
  theme_minimal()

The previous slide

## Code chunks and explicit caching

```{r}
#| output: asis

answer <- 12 * 234
```


We already saw some `markdown` code chunks. We can run inline code to calculate 
that `12 * 234` is ` r answer ` by using `r  ` and code chunks to evaluate 
larger blocks of code, such as:

```{{r}}
#| output: asis
#| label: fig-mpg
#| fig-cap: "City and highway mileage for 38 popular models of cars."
#| fig-subcap:
#|   - "Color by number of cylinders"
#|   - "Color by engine displacement, in liters"
#| layout-ncol: 2
#| column: page
#| code-fold: true
library(ggplot2)

ggplot(mpg, aes(x = hwy, y = cty, color = cyl)) +
  geom_point(alpha = 0.5, size = 2) +
  scale_color_viridis_c() +
  theme_minimal()

ggplot(mpg, aes(x = hwy, y = cty, color = displ)) +
  geom_point(alpha = 0.5, size = 2) +
  scale_color_viridis_c(option = "E") +
  theme_minimal()
```

Diagrams

```{{mermaid}}
%%| echo: false
flowchart LR
  A[Hard edge] --> B(Round edge)
  B --> C{Decision}
  C <--> D[Result one]
  C --> E[Result two]
```

flowchart LR
  A[Hard edge] --> B(Round edge)
  B --> C{Decision}
  C <--> D[Result one]
  C --> E[Result two]

See Quarto Diagrams for a more comprehensive overview of all graphing engines. This Live online Mermaid editor is awesome!

Explicit Caching

Explicit caching is a technique used in RMarkdown and Quarto documents to store and reuse the results of expensive computations or data processing steps.
It helps improve the efficiency of document generation and ensures reproducibility by avoiding redundant calculations.

Why Use Explicit Caching?

Efficiency: Caching saves time and resources by avoiding the repeated execution of time-consuming code chunks.
Reproducibility: Explicit caching ensures that the same results are used when rendering the document, even if the code is modified or re-executed.
Version Control: Caching reduces the size of version-controlled documents by storing intermediate results separately.

How Does Explicit Caching Work?

In RMarkdown and Quarto, you can use cache directives to specify which code chunks should be cached and which should not.

Cached chunks are identified by a unique hash based on their code content, ensuring consistency.

The following code block

```{r}
#| cache: true
#| code-line-numbers: true
#| label: imputation
library(mice)
library(purrr)
imp <- nhanes |> mice(print = FALSE)
imp |>
  complete("all") |> 
  map(~lm(bmi ~ chl + hyp, data = .x)) |>
  pool()
```

would result in this output

library(mice)
library(purrr)
imp <- nhanes |> mice(print = FALSE)
imp |>
  complete("all") |> 
  map(~lm(bmi ~ chl + hyp, data = .x)) |>
  pool()

Class: mipo    m = 5 
         term m    estimate         ubar            b            t dfcom
1 (Intercept) 5 21.45605903 1.915699e+01 1.402210e+01 35.983502224    22
2         chl 5  0.02839181 4.899983e-04 3.279664e-04  0.000883558    22
3         hyp 5 -0.37459963 5.312899e+00 1.142413e+00  6.683794662    22
         df       riv    lambda       fmi
1  6.781025 0.8783488 0.4676175 0.5764778
2  7.210259 0.8031857 0.4454259 0.5540567
3 13.760276 0.2580315 0.2051073 0.2999617

with corresponding cache structure relative to the root and format of the output file:

`reprex` and `renv`

library(renv)
#> 
#> Attaching package: 'renv'
#> The following objects are masked from 'package:stats':
#> 
#>     embed, update
#> The following objects are masked from 'package:utils':
#> 
#>     history, upgrade
#> The following objects are masked from 'package:base':
#> 
#>     autoload, load, remove
renv::init()
#> - Linking packages into the project library ... Done!
#> The following package(s) will be updated in the lockfile:
#> 
#> # CRAN -----------------------------------------------------------------------
#> - backports       [* -> 1.4.1]
#> - base64enc       [* -> 0.1-3]
#> - broom           [* -> 1.0.4]
#> - bslib           [* -> 0.4.2]
#> - cachem          [* -> 1.0.8]
#> - cli             [* -> 3.6.1]
#> - cpp11           [* -> 0.4.3]
#> - digest          [* -> 0.6.31]
#> - dplyr           [* -> 1.1.2]
#> - ellipsis        [* -> 0.3.2]
#> - evaluate        [* -> 0.21]
#> - fansi           [* -> 1.0.4]
#> - fastmap         [* -> 1.1.1]
#> - fontawesome     [* -> 0.5.1]
#> - fs              [* -> 1.6.2]
#> - generics        [* -> 0.1.3]
#> - glue            [* -> 1.6.2]
#> - highr           [* -> 0.10]
#> - htmltools       [* -> 0.5.5]
#> - jquerylib       [* -> 0.1.4]
#> - jsonlite        [* -> 1.8.4]
#> - knitr           [* -> 1.43]
#> - lattice         [* -> 0.21-8]
#> - lifecycle       [* -> 1.0.3]
#> - magrittr        [* -> 2.0.3]
#> - memoise         [* -> 2.0.1]
#> - mime            [* -> 0.12]
#> - pillar          [* -> 1.9.0]
#> - pkgconfig       [* -> 2.0.3]
#> - purrr           [* -> 1.0.1]
#> - R6              [* -> 2.5.1]
#> - rappdirs        [* -> 0.3.3]
#> - Rcpp            [* -> 1.0.11]
#> - RcppArmadillo   [* -> 0.12.4.0.0]
#> - renv            [* -> 1.0.2]
#> - rlang           [* -> 1.1.1]
#> - rmarkdown       [* -> 2.22]
#> - sass            [* -> 0.4.6]
#> - stringi         [* -> 1.7.12]
#> - stringr         [* -> 1.5.0]
#> - tibble          [* -> 3.2.1]
#> - tidyr           [* -> 1.3.0]
#> - tidyselect      [* -> 1.2.0]
#> - tinytex         [* -> 0.45]
#> - utf8            [* -> 1.2.3]
#> - vctrs           [* -> 0.6.2]
#> - withr           [* -> 2.5.0]
#> - xfun            [* -> 0.39]
#> - yaml            [* -> 2.3.7]
#> 
#> # GitHub ---------------------------------------------------------------------
#> - mice            [* -> gerkovink/mice@match_conditional]
#> 
#> The version of R recorded in the lockfile will be updated:
#> - R               [* -> 4.3.0]
#> 
#> - Lockfile written to "/private/var/folders/yx/6rn4qpl13wsgk4c7s3jc9d1r0000gp/T/RtmpMqetyA/reprex-1233f6d959b19-drear-kitty/renv.lock".
library(mice)
#> 
#> Attaching package: 'mice'
#> The following object is masked from 'package:stats':
#> 
#>     filter
#> The following objects are masked from 'package:base':
#> 
#>     cbind, rbind
library(magrittr)
library(purrr)
#> 
#> Attaching package: 'purrr'
#> The following object is masked from 'package:magrittr':
#> 
#>     set_names
#> The following object is masked from 'package:renv':
#> 
#>     modify
renv::snapshot()
#> - The lockfile is already up to date.
renv::remove("mice")
#> - Removing package(s) from project library ...
#> Removing package 'mice' ... Done!
renv::restore()
#> The following package(s) will be updated:
#> 
#> # GitHub ---------------------------------------------------------------------
#> - mice   [* -> gerkovink/mice@match_conditional]
#> 
#> # Installing packages --------------------------------------------------------
#> - Installing mice ...                           OK [linked from cache]
imp <- nhanes |> mice(print = FALSE)
imp |>
  complete("all") |> 
  map(~.x %$% lm(bmi ~ chl + hyp)) |>
  pool()
#> Class: mipo    m = 5 
#>          term m   estimate         ubar            b            t dfcom
#> 1 (Intercept) 5 20.6687887 2.020809e+01 8.8963873222 3.088375e+01    22
#> 2         chl 5  0.0354435 5.409979e-04 0.0001081414 6.707676e-04    22
#> 3         hyp 5 -0.8447582 4.353337e+00 0.9275040571 5.466342e+00    22
#>          df       riv    lambda       fmi
#> 1  9.489411 0.5282868 0.3456725 0.4504537
#> 2 14.161181 0.2398710 0.1934645 0.2874598
#> 3 13.811544 0.2556671 0.2036105 0.2983537

sessionInfo()
#> R version 4.3.0 (2023-04-21)
#> Platform: aarch64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.6
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
#> 
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> time zone: Europe/Amsterdam
#> tzcode source: internal
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] purrr_1.0.1    magrittr_2.0.3 mice_3.15.3    renv_1.0.2    
#> 
#> loaded via a namespace (and not attached):
#>  [1] vctrs_0.6.2      cli_3.6.1        knitr_1.43       rlang_1.1.1     
#>  [5] xfun_0.39        generics_0.1.3   glue_1.6.2       backports_1.4.1 
#>  [9] htmltools_0.5.5  fansi_1.0.4      rmarkdown_2.22   grid_4.3.0      
#> [13] evaluate_0.21    tibble_3.2.1     fastmap_1.1.1    yaml_2.3.7      
#> [17] lifecycle_1.0.3  compiler_4.3.0   dplyr_1.1.2      fs_1.6.2        
#> [21] pkgconfig_2.0.3  Rcpp_1.0.11      tidyr_1.3.0      rstudioapi_0.14 
#> [25] lattice_0.21-8   digest_0.6.31    R6_2.5.1         tidyselect_1.2.0
#> [29] reprex_2.0.2     utf8_1.2.3       pillar_1.9.0     tools_4.3.0     
#> [33] withr_2.5.0      broom_1.0.4

^{Created on 2023-09-27 with reprex v2.0.2}

Benefits of Using `renv`

1. Dependency Isolation: renv creates a dedicated environment for each project, preventing conflicts between package versions.
2. Reproducibility: With renv, you can capture and record the specific package versions used in your project, ensuring reproducibility over time.
3. Collaboration: Share your project with others, and they can easily recreate the same environment using renv.lock.
4. Easier Package Installation: renv simplifies package installation. Just run renv::restore() to set up the project environment.
5. Automatic Snapshot: renv automatically generates a renv.lock file, listing all package versions used, making it easy to recreate the environment.
6. Compatibility: renv is compatible with popular version control systems like Git, facilitating collaboration and sharing.

Use renv::snapshot() to update the renv.lock file when you add or update packages. You can use renv::activate() and renv::deactivate() to activate or deactivate renv for your project.

Scientific publishing with `rticles` and `Quarto`

To use rticles from RStudio, you can access the templates through File -> New File -> R Markdown. This will open the dialog box where you can select from one of the available templates:

Quarto

The quarto use template command can be used to create an article from one the below formats.

Terminal

quarto use template quarto-journals/acm
quarto use template quarto-journals/plos
quarto use template quarto-journals/elsevier
quarto use template quarto-journals/acs
quarto use template quarto-journals/jss

Citations

## Citations

@eval and Cai, Van Buuren and Vink [-@cai2023graphical] are some of our team's most recent publications. Some references - such as some work by Reinder Banning and Gerko Vink are much older [-@banningvink] or contain simple and contemporary solutions by @vigntg [p. 2-4] or cool but potentially confusing images [@schouten2021dance, 1255].

::: footer
But you can also refer to @eval here
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Oberman and Vink (2023) and Cai, Van Buuren and Vink (2023) are some of our team’s most recent publications. Some references - such as some work by Reinder Banning and Gerko Vink are much older (2010) or contain simple and contemporary solutions by Volker and Vink (2022, 2–4) or cool but potentially confusing images (Schouten and Vink 2021, 1255).

Banning, Reinder, and Gerko Vink. 2010. “Methoden En Indicatoren Voor Het Evalueren En Beschrijven van Controle- En Correctieprocessen.” CBS nota DMV-2010- 06-08-RBNG-GVNK. Centraal Bureau voor de Statistiek.

Cai, Mingyang, Stef van Buuren, and Gerko Vink. 2023. “Graphical and Numerical Diagnostic Tools to Assess Multiple Imputation Models by Posterior Predictive Checking.” Heliyon.

Oberman, Hanne I., and Gerko Vink. 2023. “Toward a Standardized Evaluation of Imputation Methodology.” Biometrical Journal, 2200107. https://doi.org/https://doi.org/10.1002/bimj.202200107.

Schouten, Rianne Margaretha, and Gerko Vink. 2021. “The Dance of the Mechanisms: How Observed Information Influences the Validity of Missingness Assumptions.” Sociological Methods & Research 50 (3): 1243–58.

Volker, Thom, and Gerko Vink. 2022. “Synthetic Data in r: Generating Synthetic Data with High Utility Using Mice.” Https://Www.gerkovink.com/miceVignettes/Synthetic/Synthetic.html.

Take aways

Quarto markdown is tool to integrate text, figures, code and output
Quarto enables data scientists/statisticians to communicate (re)productively
There is a universe of plugins and add-ons to make your academic life easier

All flavors markdown

Disclaimer

Today’s lecture

Take aways

What is Markdown?

Markdown basics

Markdown flavours

Popular Code Editors (for R)

RStudio

RStudio - Integrated Development Environment (IDE) for R

Popular Notebook Environments

Structure of a Quarto Document

Quarto: the YAML header

Quarto: the YAML header

Text in Quarto

Header

Subheader

Subsubheader

A single # denotes a section in slides

A double ## denotes a slide

Equations

Equations

Figures

Columns, Sizing, Equations and proper centering

F-ing it all up

Math-ing it all up

Tables

R figures

R figures

R figures

R figures

Code chunks

The previous slide

Diagrams

Explicit Caching

Why Use Explicit Caching?

How Does Explicit Caching Work?

reprex and renv

Benefits of Using renv

Scientific publishing with rticles and Quarto

Quarto

Citations

Take aways

Popular Code Editors (for `R`)

`RStudio` - Integrated Development Environment (IDE) for `R`

Text in `Quarto`

`R` figures

`R` figures

`R` figures

`R` figures

`reprex` and `renv`

Benefits of Using `renv`

Scientific publishing with `rticles` and `Quarto`